1 research outputs found
Acoustic SLAM based on the Direction-of-Arrival and the Direct-to-Reverberant Energy Ratio
This paper proposes a new method that fuses acoustic measurements in the
reverberation field and low-accuracy inertial measurement unit (IMU) motion
reports for simultaneous localization and mapping (SLAM). Different from
existing studies that only use acoustic data for direction-of-arrival (DoA)
estimates, the source's distance from sensors is calculated with the
direct-to-reverberant energy ratio (DRR) and applied as a new constraint to
eliminate the nonlinear noise from motion reports. A particle filter is applied
to estimate the critical distance, which is key for associating the source's
distance with the DRR. A keyframe method is used to eliminate the deviation of
the source position estimation toward the robot. The proposed DoA-DRR acoustic
SLAM (D-D SLAM) is designed for three-dimensional motion and is suitable for
most robots. The method is the first acoustic SLAM algorithm that has been
validated on a real-world indoor scene dataset that contains only acoustic data
and IMU measurements. Compared with previous methods, D-D SLAM has acceptable
performance in locating the robot and building a source map from a real-world
indoor dataset. The average location accuracy is 0.48 m, while the source
position error converges to less than 0.25 m within 2.8 s. These results prove
the effectiveness of D-D SLAM in real-world indoor scenes, which may be
especially useful in search and rescue missions after disasters where the
environment is foggy, i.e., unsuitable for light or laser irradiation